ArticleBioinformatics (Oxford, England)2018
A novel data structure to support ultra-fast taxonomic classification of metagenomic sequences with k-mer signatures.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Application and Comparison of Machine Learning and Database-Based Methods in Taxonomic Classification of High-Throughput Sequencing Data.Genome biology and evolution · 2024Article
- cgMSI: pathogen detection within species from nanopore metagenomic sequencing data.BMC bioinformatics · 2023Article
- Exercise and microbiome: From big data to therapy.Computational and structural biotechnology journal · 2023Review
- Nanopore sequencing of a monkeypox virus strain isolated from a pustular lesion in the Central African Republic.Scientific reports · 2022Article
- SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning.Genome biology · 2022Article
- Fast and accurate metagenotyping of the human gut microbiome with GT-Pro.Nature biotechnology · 2022Article
- Data Structures to Represent a Set ofACM computing surveys · 2022Article
- Orchestrating an Optimized Next-Generation Sequencing-Based Cloud Workflow for Robust Viral Identification during Pandemics.Biology · 2021Article
- Application of Deep Learning in Plant-Microbiota Association Analysis.Frontiers in genetics · 2021Review
- Specific Microbial Taxa and Functional Capacity Contribute to Chicken Abdominal Fat Deposition.Frontiers in microbiology · 2021Article
- Fast and Accurate Classification of Meta-Genomics Long Reads With deSAMBA.Frontiers in cell and developmental biology · 2021Article
- IDseq-An open source cloud-based pipeline and analysis service for metagenomic pathogen detection and monitoring.GigaScience · 2020Article
- Mismatch-tolerant, alignment-free sequence classification using multiple spaced seeds and multiindex Bloom filters.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- To Petabytes and beyond: recent advances in probabilistic and signal processing algorithms and their application to metagenomics.Nucleic acids research · 2020Article
- Microbial Diversity and Metabolic Potential in the Stratified Sansha Yongle Blue Hole in the South China Sea.Scientific reports · 2020Article
- UniqueInternational journal of molecular sciences · 2020Article
- Review
- Strain-level metagenomic assignment and compositional estimation for long reads with MetaMaps.Nature communications · 2019Article
- SeqOthello: querying RNA-seq experiments at scale.Genome biology · 2018Article
- Sc-ncDNAPred: A Sequence-Based Predictor for Identifying Non-coding DNA inFrontiers in microbiology · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Motivation: Metagenomic read classification is a critical step in the identification and quantification of microbial species sampled by high-throughput sequencing. Although many algorithms have been developed to date, they suffer significant memory and/or computational costs. Due to the growing popularity of metagenomic data in both basic science and clinical applications, as well as the increasing volume of data being generated, efficient and accurate algorithms are in high demand. Results: We introduce MetaOthello, a probabilistic hashing classifier for metagenomic sequencing reads. The algorithm employs a novel data structure, called l-Othello, to support efficient querying of a taxon using its k-mer signatures. MetaOthello is an order-of-magnitude faster than the current state-of-the-art algorithms Kraken and Clark, and requires only one-third of the RAM. In comparison to Kaiju, a metagenomic classification tool using protein sequences instead of genomic sequences, MetaOthello is three times faster and exhibits 20-30% higher classification sensitivity. We report comparative analyses of both scalability and accuracy using a number of simulated and empirical datasets. Availability and implementation: MetaOthello is a stand-alone program implemented in C ++. The current version (1.0) is accessible via https://doi.org/10.5281/zenodo.808941. Contact: liuj@cs.uky.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.